学术期刊引文不规范问题及对策分析

当今,学术界的引文不规范行为十分严重。针对于此,本文主要概括了引文不规范行为的分类、原因和解决对策,强调引文在科技论文中的重要作用,并从作者自身、期刊社和相关法律制度三个方面解决该问题。     

Predicting bid prices by using machine learning methods

It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder’s bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.     

Does the Delphi process lead to increased accuracy in group-based judgmental forecasts or does it simply induce consensus amongst judgmental forecasters?

We investigate the relative impact of internal Delphi process factors - including panelists' degree of confidence, expertise, majority/minority positioning - and an external factor, richness of feedback - on opinion change and subsequent accuracy of judgmental forecasts. We found that panelists who had low confidence in their judgmental forecast and/or who were in a minority were more likely to change their opinion than those who were more confident and/or in a majority. The addition of rationales, or reasons, to the numeric feedback had little impact upon panelists' final forecasts, despite the quality of panelists' rationales being significantly positively correlated with accurate forecasts and thus of potential use to aid forecast improvement over Delphi rounds. Rather, the effect of rationales was similar to that of confidence: to pull panelists towards the majority opinion regardless of its correctness. We conclude that majority opinion is the strongest influence on panelists' opinion change in both the ‘standard’ Delphi, and Delphi-with-reasons. We make some suggestions for improved variants of the Delphi-with-reasons technique that should help reduce majority influence and thereby permit reasoned arguments to exert their proper pull on opinion change, resulting in forecast accuracy improvements over Delphi rounds.     

Forecasting inflation in China

This paper forecasts inflation in China over a 12-month horizon. The analysis runs 15 alternative models and finds that only those considering many predictors via a principal component display a better relative forecasting performance than the univariate benchmark.     

Forecasting Travel Patterns Using Palmore's Cohort Analysis

Summary

The purpose of this study was to introduce Palmore's (1978) method of cohort analysis and illustrate its potential application to tourism forecasting. Results suggested that (a) older cohorts participate less frequently in international travel than younger cohorts, (b) decrease in participation continues as one ages, and (c) changes in travel behavior are due primarily to period effects. With respect to the impact these findings may have on the tourism industry, the results suggest that marketers should monitor the aggregate changes taking place within targeted cohorts, and strategic planning should not be based on an assessment of differences between cohorts at one point in time.     

Developing a Forecast System for Hotel Occupancy Rate Using Integrated ARIMA Models

Abstract

International tourist hotels play a consequential role in the development of the tourism industry. The occupancy rate is usually considered a pertinent indicator in measuring the performance of the hotels. This study employs the ARIMA and ARIMA transfer function model incorporated with the Box-Cox transformation function for the forecasting of occupancy rate. The results of this research find two explanatory variables strongly affect the occupancy rate: one is the numbers of tourists and the other is the Taiwan/ Japan foreign exchange rate. The forecasting shows slow rising of the occupancy rate for the international hotels in Taiwan; it will reach 64.67% by the year 2000. The forecast of the occupancy rate provides important information tor both government agencies and hotel managers so that corresponding management strategies can be made.